• Smart Media Journal
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• v.11 no.1
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• pp.58-66
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• 2022

As Internet technology develops, SNS users are increasing. As SNS becomes popular, SNS-type crimes using the influence and anonymity of social networks are increasing day by day. In this paper, we propose a fake account classification method that applies machine learning and deep learning to statistical and image data for fake accounts classification. SNS account data used for training was collected by itself, and the collected data is based on statistical data and image data. In the case of statistical data, machine learning and multi-layer perceptron were employed to train. Furthermore in the case of image data, a convolutional neural network (CNN) was utilized. Accordingly, it was confirmed that the overall performance of account classification was significantly meaningful.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1179-1195
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• 2018

Recently, a variety of attacks on web services have been occurring through a multiple access environment such as PC, tablet, and smartphone. These attacks are causing various subsequent damages such as online fraud transactions, takeovers and theft of accounts, fraudulent logins, and information leakage through web service vulnerabilities. Creating a new fake account for Fraud attacks, hijacking accounts, and bypassing IP while using other usernames or email addresses is a relatively easy attack method, but it is not easy to detect and block these attacks. In this paper, we have studied a method to detect online fraud transaction and obsession by identifying and managing devices accessing web service using web-based device fingerprinting. In particular, it has been proposed to identify devices and to manage them by scoring process. In order to secure the validity of the proposed scheme, we analyzed the application cases and proved that they can effectively defend against various attacks because they actively cope with online fraud and obtain visibility of user accounts.